Flow simulation considering adsorption boundary layer based on digital rock and finite element method

Due to the low permeability of tight reservoirs,throats play a significant role in controlling fluid flow.Although many studies have been conducted to investigate fluid flow in throats in the microscale domain,comparatively fewer works have been devoted to study the effect of adsorption boundary layer(ABL)in throats based on the digital rock method.By considering an ABL,we investigate its effects on fluid flow.We build digital rock model based on computed tomography technology.Then,microscopic pore structures are extracted with watershed segmentation and pore geometries are meshed through Delaunay triangulation approach.Finally,using the meshed digital simulation model and finite element method,we investigate the effects of viscosity and thickness of ABL on microscale flow.Our results demonstrate that viscosity and thickness of ABL are major factors that significantly hinder fluid flow in throats.

Meteorite impact craters as hotspots for mineral resources and energy fuels:A global review

The ever-increasing recovery rate of natural resources from terrestrial impact craters over the last fewdecades across the globe offers new avenues for further exploration of mineral and hydrocarbon resources in such settings.As of today,60 of the 208 terrestrial craters have been identified to host diverseresources such as hydrocarbons,metals and construction materials.The potential of craters as plausibleresource contributors to the energy sector is therefore,worthy of consideration,as 42(70%)of the 60craters host energy resources such as oil,gas,coal,uranium,mercury,critical and major minerals as wellas hydropower resources.Among others,19 craters are of well-developed hydrocarbon reserves.Mineraldeposits associated with craters are also classified similar to other mineral resources such as progenetic,syngenetic and epigenetic sources.Of these,the progenetic and syngenetic mineralization are confinedto the early and late excavation stage of impact crater evolution,respectively,whereas epigenetic deposits are formed during and after the modification stage of crater formation.Thus,progenetic andsyngenetic mineral deposits(like Fe,Ni,Pb,Zn and Cu)associated with craters are formed as a directresult of the impact event,whereas epigenetic deposits(e.g.hydrocarbon)are hosted by the impactstructure and result from post-impact processes.In the progenetic and syngenetic deposits,the shockwave induced fracturing and melting aid the formation of deposits,whereas in the epigenetic deposits,the highly fractured lithostratigraphic units of higher porosity and permeability,like the centralelevated area(CEA)or the rim,act as traps.In this review,we provide a holistic view of the mineral andenergy resources associated with impact craters,and use some of the remote sensing techniques toidentify the mineral deposits as supplemented by a schematic model of the types of deposits formedduring cratering process.

Computer vision-based limestone rock-type classification using probabilistic neural network

Proper quality planning of limestone raw materials is an essential job of maintaining desired feed in cement plant. Rock-type identification is an integrated part of quality planning for limestone mine. In this paper, a computer vision-based rock-type classification algorithm is proposed for fast and reliable identification without human intervention. A laboratory scale vision-based model was developed using probabilistic neural network（PNN） where color histogram features are used as input. The color image histogram-based features that include weighted mean, skewness and kurtosis features are extracted for all three color space red, green, and blue. A total nine features are used as input for the PNN classification model. The smoothing parameter for PNN model is selected judicially to develop an optimal or close to the optimum classification model. The developed PPN is validated using the test data set and results reveal that the proposed vision-based model can perform satisfactorily for classifying limestone rocktypes. Overall the error of mis-classification is below 6%. When compared with other three classification algorithms, it is observed that the proposed method performs substantially better than all three classification algorithms.

A GIS tool for infinite slope stability analysis(GIS-TISSA)

Landslides are one of the most common and a destructive natural hazard in mountainous terrain and thus evaluating their potential locations and the conditions under which they may occur is crucial for their hazard assessment.Shallow landslide occurrence in soil and regolith covered slopes are often modeled using the infinite slope model,which characterizes the slope stability in terms of a factor of safety(FS) value.Different approaches have been followed to also assess and propagate uncertainty through such models.Haneberg(2004) introduced the use of the First Order Second Moment(FOSM) method to propagate input uncertainty through the infinite slope model,further developing the model and implementing it in the PISA-m software package(Haneberg,2007).Here we present an ArcPy implementation of PISA-m algorithms,which can be run from ESRI ArcMap in an entirely consistent georeferenced framework,and which we call "GIS Tool for Infinite Slope Stability Analysis"(GIS-TISSA).Users can select between different input options,e.g.,following a similar input style as for PISA-m,i.e., using an ASCII.csv parameters input file,or providing each input parameter as a raster or constant value,through the program graphic user interface.Analysis outputs can include FS mean and standard deviation estimates,the probability of failure(FS <1), and a reliability index(RI) calculation for FS.Following the same seismic analysis approach as in PISA-m, the Newmark acceleration can also be done,for which raster files of the mean,standard deviation,probability of exceedance,and RI are also generated.Verification of the code is done by replicating the results obtained with the PISA-m code for different input options,within a 10-5 relative error.Monte Carlo modeling is also applied to validate GIS-TISSA outputs,showing a good overall correspondence.A case study was performed for Kannur district,Kerala,India,where an extensive landslide databa se for the year 2018 was available.81.19% of the actual landslides fell in zones identified by the model as unstable.GIS-TISSA provides a user-friendly interface,particularly for those users familiar with ESRI ArcMap,that is fully embedded in a GIS framework and which can then be used for further analysis without having to change software platforms and do data conversions.The ArcPy toolbox is provided as a.pyt file as an appendix as well as hosted at the weblink:https://pages.mtu.edu/~toommen/GeoHazard.html.

InSAR as a tool for monitoring hydropower projects:A review

This paper provides a review of using Interferometric Synthetic Aperture Radar(InSAR),a microwave remote sensing technique,for deformation monitoring of hydroelectric power projects,a critical infrastructure that requires consistent and reliable monitoring.Almost all major dams around the world were built for the generation of hydropower.InSAR can enhance dam safety by providing timely settlement measurements at high spatial-resolution.This paper provides a holistic view of different InSAR deformation monitoring techniques such as Differential Synthetic Aperture Radar Interferometry(DInSAR),Ground-Based Synthetic Aperture Radar(GBInSAR),Persistent Scatterer Interferometric Synthetic Aperture Radar(PSInSAR),Multi-Temporal Interferometric Synthetic Aperture Radar(MTInSAR),QuasiPersistent Scatterer Interferometric Synthetic Aperture Radar(QPSInSAR)and Small BAseline Subset(SBAS).PSInSAR,GBInSAR,MTInSAR,and DInSAR techniques were quite commonly used for deformation studies.These studies demonstrate the advantage of InSAR-based techniques over other conventional methods,which are laborious,costly,and sometimes unachievable.InSAR technology is also favoured for its capability to provide monitoring data at all times of day or night,in all-weather conditions,and particularly for wide areas with mm-scale precision.However,the method also has some disadvantages,such as the maximum deformation rate that can be monitored,and the location for monitoring cannot be dictated.Through this review,we aim to popularize InSAR technology to monitor the deformation of dams,which can also be used as an early warning method to prevent any unprecedented catastrophe.This study also discusses some case studies from southern India to demonstrate the capabilities of InSAR to indirectly monitor dam health.

Remote sensing for energy resources:Introduction

Energy resources are critical for human existence.The energy that we use is obtained from the Earth,its atmosphere,or the Sun.Some of these energy resources we obtain from the subsurface through the mining or extraction process,like gas,coal,oil,geothermal,and uranium.Furthermore,we also harness from the surface like wind,solar,and tidal.Geoscientists and geological engineers play a critical role in developing energy resources,maintaining its operation,and evaluating their environmental impacts.In recent years,the advancement in satellite and aerial remote sensing is a tool that is widely used by geoscientists and engineers to address various aspects of energy resources.The application of remote sensing to energy resources,starting from reconnaissance to detailed studies,utilizing multispectral to hyperspectral and radar to LiDAR techniques,have gained significant importance.Some remote sensing applications include identifying potential source of fuels used in the energy.